scholarly journals Superspreading of airborne pathogens in a heterogeneous world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julius B. Kirkegaard ◽  
Joachim Mathiesen ◽  
Kim Sneppen
Keyword(s):  
University Students ◽  
Spatial Heterogeneity ◽  
Short Duration ◽  
Power Law ◽  
Solvable Model ◽  
Pathogen Transmission ◽  
Single Individual ◽  
Tracking Data ◽  
Extreme Statistics ◽  
Social Events

AbstractEpidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

scholarly journals Airborne Pathogens in a Heterogeneous World: Superspreading & Mitigation

2020 ◽  
Author(s):  
Julius B. Kirkegaard ◽  
Joachim Mathiesen ◽  
Kim Sneppen
Keyword(s):  
University Students ◽  
Spatial Heterogeneity ◽  
Short Duration ◽  
Power Law ◽  
Solvable Model ◽  
Pathogen Transmission ◽  
Single Individual ◽  
Tracking Data ◽  
Extreme Statistics ◽  
Social Events

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20 % infecting more than 80 %, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

scholarly journals “I Spent Most of Freshers in my Room”—A Qualitative Study of the Social Experiences of University Students on the Autistic Spectrum

2021 ◽  
Author(s):  
Helen Goddard ◽  
Anna Cook
Keyword(s):  
University Students ◽  
Social Isolation ◽  
Social Life ◽  
Social Experiences ◽  
Mental Wellbeing ◽  
Structured Interviews ◽  
Social Lives ◽  
Social Events ◽  
University Life ◽  
The Impact

AbstractAutistic university students face extra challenges in both their academic and social life. Barriers to socialising appear to be less well understood and supported by universities than academic requirements. Semi-structured interviews were conducted with ten autistic university students to explore their social experiences. Questions explored their social experiences, satisfaction with social life, disclosure of ASD to others, and the impact of mental wellbeing on university life. Thematic analysis indicated most participants were unsatisfied with their social lives and experienced mental health issues. Factors exacerbating social isolation included lack of suitable social events, lack of social support and feeling unable to disclose to peers. Factors which reduced social isolation included joining an autism or special interest society and receiving social mentoring.

scholarly journals Spatial Heterogeneity of the Incidence of Powdery Mildew on Hop Cones

Plant Disease ◽  
2006 ◽  
Vol 90 (11) ◽  
pp. 1433-1440 ◽  
Author(s):  
David H. Gent ◽  
Walter F. Mahaffee ◽  
William W. Turechek
Keyword(s):  
Powdery Mildew ◽  
Spatial Heterogeneity ◽  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Disease Assessment ◽  
Cluster Sampling ◽  
Data Sets ◽  
Ratio Test ◽  
Frequency Distributions

The spatial heterogeneity of the incidence of hop cones with powdery mildew (Podosphaera macularis) was characterized from transect surveys of 41 commercial hop yards in Oregon and Washington from 2000 to 2005. The proportion of sampled cones with powdery mildew ( p) was recorded for each of 221 transects, where N = 60 sampling units of n = 25 cones assessed in each transect according to a cluster sampling strategy. Disease incidence ranged from 0 to 0.92 among all yards and dates. The binomial and beta-binomial frequency distributions were fit to the N sampling units in a transect using maximum likelihood. The estimation procedure converged for 74% of the data sets where p > 0, and a loglikelihood ratio test indicated that the beta-binomial distribution provided a better fit to the data than the binomial distribution for 46% of the data sets, indicating an aggregated pattern of disease. Similarly, the C(α) test indicated that 54% could be described by the beta-binomial distribution. The heterogeneity parameter of the beta-binomial distribution, θ, a measure of variation among sampling units, ranged from 0.01 to 0.20, with a mean of 0.037 and a median of 0.015. Estimates of the index of dispersion ranged from 0.79 to 7.78, with a mean of 1.81 and a median of 1.37, and were significantly greater than 1 for 54% of the data sets. The binary power law provided an excellent fit to the data, with slope and intercept parameters significantly greater than 1, which indicated that heterogeneity varied systematically with the incidence of infected cones. A covariance analysis indicated that the geographic location (region) of the yards and the type of hop cultivar had little effect on heterogeneity; however, the year of sampling significantly influenced the intercept and slope parameters of the binary power law. Significant spatial autocorrelation was detected in only 11% of the data sets, with estimates of first-order autocorrelation, r1, ranging from -0.30 to 0.70, with a mean of 0.06 and a median of 0.04; however, correlation was detected in only 20 and 16% of the data sets by median and ordinary runs analysis, respectively. Together, these analyses suggest that the incidence of powdery mildew on cones was slightly aggregated among plants, but patterns of aggregation larger than the sampling unit were rare (20% or less of data sets). Knowledge of the heterogeneity of diseased cones was used to construct fixed sampling curves to precisely estimate the incidence of powdery mildew on cones at varying disease intensities. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and management decisions.

scholarly journals Spatial Heterogeneity Analysis of Short-Duration Extreme Rainfall Events in Megacities in China

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3364 ◽  
Author(s):  
Qi Zhuang ◽  
Shuguang Liu ◽  
Zhengzheng Zhou
Keyword(s):  
Spatial Heterogeneity ◽  
Short Duration ◽  
Urban Areas ◽  
Spatial Association ◽  
Extreme Rainfall ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Design Storm

Given the fact that researchers require more specific spatial rainfall information for storm flood calculation, hydrological risk assessment, and water budget estimates, there is a growing need to analyze the spatial heterogeneity of rainfall accurately. This paper provides insight into rainfall spatial heterogeneity in urban areas based on statistical analysis methods. An ensemble of short-duration (3-h) extreme rainfall events for four megacities in China are extracted from a high-resolution gridded rainfall dataset (resolution of 30 min in time, 0.1° × 0.1° in space). Under the heterogeneity framework using Moran’s I, LISA (Local Indicators of Spatial Association), and semi-variance, the multi-scale spatial variability of extreme rainfall is identified and assessed in Shanghai (SH), Beijing (BJ), Guangzhou (GZ), and Shenzhen (SZ). The results show that there is a pronounced spatial heterogeneity of short-duration extreme rainfall in the four cities. Heterogeneous characteristics of rainfall within location, range, and directions are closely linked to the different urban growth in four cities. The results also suggest that the spatial distribution of rainfall cannot be neglected in the design storm in urban areas. This paper constitutes a useful contribution to quantifying the degree of spatial heterogeneity and supports an improved understanding of rainfall/flood frequency analysis in megacities.

scholarly journals Spatial heterogeneity and variability of a large-scale vegetation community using a power-law model

2005 ◽  
Vol 10 (4) ◽  
pp. 469-477 ◽  
Author(s):  
Zhiyuan Song ◽  
Darning Huang ◽  
Masae Shiyomi ◽  
Yusheng Wang ◽  
Shiqeo Takahashi ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Power Law ◽  
Large Scale ◽  
Power Law Model ◽  
Vegetation Community

scholarly journals Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology

2018 ◽  
Vol 115 (28) ◽  
pp. 7374-7379 ◽  
Author(s):  
Lauren A. White ◽  
James D. Forester ◽  
Meggan E. Craft
Keyword(s):  
Spatial Heterogeneity ◽  
Landscape Structure ◽  
Spatial Organization ◽  
Complex Disease ◽  
Animal Movement ◽  
Pathogen Transmission ◽  
Movement Behavior ◽  
Disease Dynamics ◽  
Conspecific Density ◽  
Pathogen Persistence

Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host–pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.

scholarly journals Spectral properties of gamma-ray bursts observed by the Suzaku wide-band all-sky monitor

2019 ◽  
Vol 71 (4) ◽  
Author(s):  
Norisuke Ohmori ◽  
Kazutaka Yamaoka ◽  
Makoto Yamauchi ◽  
Yuji Urata ◽  
Masanori Ohno ◽  
...  
Keyword(s):  
Short Duration ◽  
Power Law ◽  
Spectral Properties ◽  
Gamma Ray ◽  
Wide Band ◽  
High Energy ◽  
Gamma Ray Bursts ◽  
Sky Monitor ◽  
S Peak ◽  
Single Power

Abstract We have systematically studied the spectral properties of 302 localized gamma-ray bursts (GRBs) observed by the Suzaku wide-band all-sky monitor (WAM) from 2005 August to 2010 December. The energy spectra in the 100–5000 keV range integrated over the entire emission and the 1 s peak were fitted by three models: a single power law, a power law with an exponential cutoff (CPL), and the GRB Band function (GRB). Most of the burst spectra were well fitted by a single power law. The average photon index α was −2.11 and −1.73 for long and short bursts, respectively. For the CPL and GRB models, the low-energy and high-energy photon indices (α and β) for the entire emission spectra were consistent with previous measurements. The averages of the α and β were −0.90 and −2.65 for long-duration GRBs, while the average α was −0.55 and the β was not well constrained for short-duration GRBs. However, the average peak energy Epeak was 645 and 1286 keV for long- and short-duration GRBs respectively, which are higher than previous Fermi/GBM measurements (285 keV and 736 keV). The α and Epeak of the 1 s peak spectra were larger, i.e., the spectra were harder, than the total fluence spectra. Spectral simulations based on Fermi-GBM results suggest that the higher Epeaks measured by the Suzaku WAM could be due to detector selection bias, mainly caused by the limited energy range above 100 keV.

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